private void PredictionNuThread(ref int count, SENSOR sensor, int start, int end, ref ConcurrentStack<SVMParameter> svmParams, List<SVMNode[]> data, int svmCount, ref NoveltyResult bestCoveredResult, ref Mutex mutex, ref ConcurrentBag<string> nuResults)
{
OneClassClassifier occ = new OneClassClassifier(data);
List<OneClassFV> anomali = new List<OneClassFV>();
List<Events> eventResult = new List<Events>();
int maxCount = svmCount;
string sensorPath = path + "/" + sensor.ToString();
foreach (Events p in events)
{
var evt = p.Copy();
eventResult.Add(evt);
}
while (!svmParams.IsEmpty)
{
SVMParameter svmParam = null;
svmParams.TryPop(out svmParam);
if (svmParam == null)
{
break;
}
anomali = new List<OneClassFV>();
occ.CreateModel(svmParam);
anomali.AddRange(occ.PredictOutliers(featureVectors[sensor].Where(x => start < x.TimeStamp && x.TimeStamp < end).ToList()));
PointsOfInterest dPointsOfInterest = new PointsOfInterest(anomali);
foreach (Events evt in eventResult)
{
evt.SetPointOfInterest(dPointsOfInterest);
}
NoveltyResult tempResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
/*NoveltyResult.ConfusionMatrix cfm = tempResult.CalculateConfusionMatrix();
decimal tpr = ((decimal)cfm.TruePostive) / ((decimal)cfm.TruePostive + cfm.FalseNegative);
decimal fpr = 1 - ((decimal)cfm.TrueNegative / ((decimal)cfm.TrueNegative + cfm.FalsePostive));
*/
double temp = tempResult.FlaggedAreaSize();
double temp2 = tempResult.CalculateTotalNormalArea();
double areaCovered = ((double)tempResult.FlaggedAreaSize() / tempResult.CalculateTotalNormalArea() > 1) ? 1 : tempResult.FlaggedAreaSize() / (double)tempResult.CalculateTotalNormalArea();
nuResults.Add($"{tempResult.parameter.Nu.ToString()}:"
+ $"{tempResult.CalculateHitResult().eventHits/ (double)tempResult.CalculateHitResult().eventsTotal};"
+ $"{tempResult.CalculateHitResult().hits / ((double)tempResult.CalculateHitResult().misses+ tempResult.CalculateHitResult().hits)};"
+ $"{areaCovered}");
mutex.WaitOne();
count++;
SetProgress(count, sensor);
mutex.ReleaseMutex();
}
Log.LogMessage(sensor + " done!");
}
private void PredictionThread(ref int count, SENSOR sensor, int start, int end, ref ConcurrentStack<SVMParameter> svmParams, List<SVMNode[]> data, int svmCount, ref NoveltyResult bestCoveredResult, ref Mutex mutex)
{
OneClassClassifier occ = new OneClassClassifier(data);
List<OneClassFV> anomali = new List<OneClassFV>();
List<Events> eventResult = new List<Events>();
List<OneClassFV> outliersFromSam = new List<OneClassFV>();
int maxCount = svmCount;
string sensorPath = path + "/" + sensor.ToString();
foreach (Events p in events)
{
var evt = p.Copy();
eventResult.Add(evt);
}
while (!svmParams.IsEmpty)
{
SVMParameter svmParam = null;
svmParams.TryPop(out svmParam);
if (svmParam == null)
{
break;
}
anomali = new List<OneClassFV>();
occ.CreateModel(svmParam);
anomali.AddRange(occ.PredictOutliers(featureVectors[sensor].Where(x => start < x.TimeStamp && x.TimeStamp < end).ToList()));
PointsOfInterest dPointsOfInterest = new PointsOfInterest(anomali);
foreach (Events evt in eventResult)
{
evt.SetPointOfInterest(dPointsOfInterest);
}
NoveltyResult tempResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
/*NoveltyResult.ConfusionMatrix cfm = tempResult.CalculateConfusionMatrix();
decimal tpr = ((decimal)cfm.TruePostive) / ((decimal)cfm.TruePostive + cfm.FalseNegative);
decimal fpr = 1 - ((decimal)cfm.TrueNegative / ((decimal)cfm.TrueNegative + cfm.FalsePostive));
*/
mutex.WaitOne();
if (bestCoveredResult == null)
{
bestCoveredResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali);
}
else if (tempResult.CalculateCoveredScore() > bestCoveredResult.CalculateCoveredScore())
{
//bestResult = new NoveltyResult(dPointsOfInterest, eventResult, start, end, svmParam, anomali); ;
bestCoveredResult = tempResult;
}
count++;
SetProgress(count, sensor);
mutex.ReleaseMutex();
}
Log.LogMessage(sensor + " done!");
}